We provide an automated script that installs third party analysis tools,
required genome data and python library dependencies for running human variant
and RNA-seq analysis, bundled into an isolated directory or virtual environment:

bcbio should install cleanly on Linux systems. For Mac OSX, we suggest
trying bcbio-vm which runs
bcbio on Amazon Web Services or isolates all the third party tools inside a
Docker container. bcbio-vm is still a work in progress but not all of the
dependencies bcbio uses install cleanly on OSX.

With the command line above, indexes and associated data files go in
/usr/local/share/bcbio-nextgen and tools are in /usr/local. If you don’t
have write permissions to install into the /usr/local directories you can
install in a user directory like ~/local or use sudochmod to give your
standard user permissions. Please don’t run the installer with sudo or as the
root user.

The installation is highly customizable, and you can install
additional software and data later using bcbio_nextgen.pyupgrade.
Run pythonbcbio_nextgen_install.py with no arguments to see options
for configuring the installation process. Some useful arguments are:

Java 1.7, needed when running GATK < 3.6 or MuTect. This must be available in
your path so typing java-version resolves a 1.7 version. bcbio
distributes Java 8 as part of the anaconda installation for recent versions of
GATK and MuTect2. You can override the Java 8 installed with bcbio by setting
BCBIO_JAVA_HOME=/path/to/your/javadir if you have the java you want in
/path/to/your/javadir/bin/java.

An OpenGL library, like Mesa (On Ubuntu/deb systems: libglu1-mesa,
On RedHat/rpm systems: mesa-libGLU-devel). This is only required for
cancer heterogeneity analysis with BubbleTree.

The automated installer creates a fully integrated environment that allows
simultaneous updates of the framework, third party tools and biological data.
This offers the advantage over manual installation of being able to manage and
evolve a consistent analysis environment as algorithms continue to evolve and
improve. Installing this way is as isolated and self-contained as possible
without virtual machines or lightweight system containers like Docker. The
Upgrade section has additional documentation on including
additional genome data for supported bcbio genomes. For genome builds not
included in the defaults, see the documentation on Adding custom genomes.
Following installation, you should edit the pre-created system configuration
file in /usr/local/share/bcbio-nextgen/galaxy/bcbio_system.yaml to match
your local system or cluster configuration (see Tuning core and memory usage).

We use the same automated installation process for performing upgrades
of tools, software and data in place. Since there are multiple targets
and we want to avoid upgrading anything unexpectedly, we have specific
arguments for each. Generally, you’d want to upgrade the code, tools
and data together with:

bcbio_nextgen.pyupgrade-ustable--tools--data

Tune the upgrade with these options:

-u Type of upgrade to do for bcbio-nextgen code. stable
gets the most recent released version and development
retrieves the latest code from GitHub.

--toolplus Specify additional tools to include. See the section on
Extra software for more details.

--genomes and --aligners options add additional aligner
indexes to download and prepare. bcbio_nextgen.pyupgrade-h lists
available genomes and aligners. If you want to install multiple genomes or
aligners at once, specify --genomes or --aligners
multiple times, like this:
--genomesGRCh37--genomesmm10--alignersbwa--alignersbowtie2

Leave out the --tools option if you don’t want to upgrade third party
tools. If using --tools, it will use the same directory as specified
during installation. If you’re using an older version that has not yet gone
through a successful upgrade or installation and saved the tool directory, you
should manually specify --tooldir for the first upgrade. You can also pass
--tooldir to install to a different directory.

Leave out the --data option if you don’t want to get any upgrades
of associated genome data.

Some aligners such as STAR don’t have pre-built indices due to the large file
sizes of these. You set the number of cores to use for indexing with
--cores8.

bcbio installs associated data files for sequence processing, and you’re able to
customize this to install larger files or change the defaults. Use the
--datatarget flag (potentially multiple times) to customize or add new
targets.

By default, bcbio will install data files for variation, rnaseq and
smallrna but you can sub-select a single one of these if you don’t require
other analyses. The available targets are:

rnaseq – Transcripts and indices for running RNA-seq. The transcript
files are also used for annotating and prioritizing structural variants.

smallrna – Data files for doing small RNA analysis.

gemini – The GEMINI framework
associates publicly available metadata with called variants, and provides
utilities for query and analysis. This target installs the required GEMINI
data files, including ExAC.

gnomad – gnomAD is a large scale
collection of genome variants, expanding on ExAC to include whole genome and
more exome inputs. This is a large 25Gb download, available for human genome
builds GRCh37, hg19 and hg38.

cadd – CADD evaluates the
potential impact of variations. It is freely available for non-commercial
research, but requires licensing for commercial usage. The download is 30Gb and
GEMINI will include CADD annotations if present.

dbnsfp Like CADD, dbNSFP
provides integrated and generalized metrics from multiple sources to help with
prioritizing variations for follow up. The files are large: dbNSFP is 10Gb,
expanding to 100Gb during preparation. VEP will use dbNSFP for annotation of
VCFs if included.

dbscsnvdbscSNV
includes all potential human SNVs within splicing consensus regions
(−3 to +8 at the 5’ splice site and −12 to +2 at the 3’ splice site), i.e. scSNVs,
related functional annotations and two ensemble prediction scores for predicting their potential of altering splicing.
VEP will use dbscSNV for annotation of VCFs if included.

For somatic analyses, bcbio includes COSMIC
v68 for hg19 and GRCh37 only. Due to license restrictions, we cannot include
updated versions of this dataset and hg38 support with the installer. To prepare
these datasets yourself you can use a utility script shipped with cloudbiolinux
that downloads, sorts and merges the VCFs, then copies into your bcbio installation:

We’re not able to automatically install some useful tools due to licensing
restrictions, so we provide a mechanism to manually download and add these to
bcbio-nextgen during an upgrade with the --toolplus command line.

bcbio includes an installation of GATK4, which is freely available for all uses.
This is the default runner for HaplotypeCaller or MuTect2. If you want to use an
older version of GATK, it requires manual installation. This is freely available
for academic users, but requires a license for commerical use. It is not freely
redistributable so requires a manual download from the GATK download site.
You also need to include tools_off:[gatk4] in your configuration for runs:
see Changing bcbio defaults.

To install GATK3, register with the pre-installed gatk bioconda wrapper:

gatk-register/path/to/GenomeAnalysisTK.tar.bz2

If you’re not using the most recent post-3.6 version of GATK, or using a nightly
build, you can add --noversioncheck to the command line to skip comparisons
to the GATK version.

bcbio-nextgen provides a wrapper around external tools and data, so the actual
tools used drive the system requirements. For small projects, it should install
on workstations or laptops with a couple Gb of memory, and then scale as needed
on clusters or multicore machines.

Disk space requirements for the tools, including all system packages are under
4Gb. Biological data requirements will depend on the genomes and aligner indices
used, but a suggested install with GRCh37 and bowtie/bwa2 indexes uses
approximately 35Gb of storage during preparation and ~25Gb after:

Most software tools used by bcbio require Java 1.8. bcbio distributes an OpenJDK
Java build and uses it so you don’t need to install anything. Older versions of
GATK (< 3.6) and MuTect require a locally installed Java 1.7. If you
have version incompatibilities, you’ll see errors like:

Unsupportedmajor.minorversion51.0

Fixing this requires either installing Java 1.7 for old GATK and MuTect or
avoiding pointing to an incorrect java (unsetJAVA_HOME). You can also tweak
the java used by bcbio, described in the Automated installation
section.

Import errors with tracebacks containing Python libraries outside of the bcbio
distribution (/path/to/bcbio/anaconda) are often due to other conflicting
Python installations. bcbio tries to isolate itself as much as possible but
external libraries can get included during installation due to the
PYTHONHOME or PYTHONPATH environmental variables or local site libraries.
These commands will temporary unset those to get bcbio installed, after which it
should ignore them automatically:

$ unset PYTHONHOME
$ unset PYTHONPATH
$ export PYTHONNOUSERSITE=1

Finally, having a .pydistutils.cfg file in your home directory can mess with
where the libraries get installed. If you have this file in your
home directory, temporarily renaming it to something else may fix
your installation issue.

The manual process does not allow the in-place updates and management of third
party tools that the automated installer makes possible. It’s a more error-prone
and labor intensive process. If you find you can’t use the installer we’d love
to hear why to make it more amenable to your system. If you’d like to develop
against a bcbio installation, see the documentation on setting up a
Development infrastructure.

The code drives a number of next-generation sequencing analysis tools
that you need to install on any machines involved in the processing. The
CloudBioLinux toolkit provides automated scripts to help with installation
for both software and associated data files:

You can also install them manually, adjusting locations in the resources
section of your bcbio_system.yaml configuration file as needed. The
CloudBioLinux infrastructure provides a full list of third party software
installed with bcbio-nextgen in `packages-conda.yaml`_, which lists all third
party tools installed through Bioconda

In addition to existing bioinformatics software the pipeline requires
associated data files for reference genomes, including pre-built indexes
for aligners. The CloudBioLinux toolkit again provides an automated
way to download and prepare these reference genomes:

The biodata.yaml file contains information about what genomes to
download. The fabricrc.txt describes where to install the genomes
by adjusting the data_files variable. This creates a tree
structure that includes a set of Galaxy-style location files to
describe locations of indexes:

Individual genome directories contain indexes for aligners in
individual sub-directories prefixed by the aligner name. This
structured scheme helps manage aligners that don’t have native Galaxy
.loc files. The automated installer will download and set this up
automatically: